Direct S3 Data Access

Summary

In the previous exercises we exercise we search for and discovered cloud data assets that met certain search criteria (i.e., interests with our region of interest and for a specified date range). In this exercise we will leverage the data access links to show how to access HLS data directly from S3.

HTTPS vs s3

NASA Eartdata cloud provides two pathways for accessing data from the cloud. The first is via HTTPS. The other is through direct S3 bucket access.

Below are some considerations when selecting … Below are some advantages to choosing one or the other…

Objective

  • Configure our notebook environment and retrieve temporary S3 credentials for in-region direct S3 bucket access
  • Access a single HLS file
  • Access and clip an HLS file to a region of interest
  • Create an HLS time series data array

Let’s get started!


Import Required Packages

%matplotlib inline
import matplotlib.pyplot as plt
from datetime import datetime
import os
import subprocess
import requests
import boto3
import pandas as pd
import numpy as np
import xarray as xr
import rasterio as rio
from rasterio.session import AWSSession
from rasterio.plot import show
import rioxarray
import geopandas
import pyproj
from pyproj import Proj
from shapely.ops import transform
import geoviews as gv
from cartopy import crs
import hvplot.xarray
import holoviews as hv
gv.extension('bokeh', 'matplotlib')

Configure Local Environment and Get Temporary Credentials

To perform direct S3 data access one needs to acquire temporary S3 credentials. The credentials give users direct access to S3 buckets in NASA Earthdata Cloud. AWS credentials should not be shared, so take precautions when using them in notebooks our scripts. Note, these temporary credentials are valid for only 1 hour. For more information regarding the temporary credentials visit https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME. A netrc file is required to aquire these credentials. Use the NASA Earthdata Authentication to create a netrc file in your home directory.

s3_cred_endpoint = 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'
def get_temp_creds():
    temp_creds_url = s3_cred_endpoint
    return requests.get(temp_creds_url).json()
temp_creds_req = get_temp_creds()
#temp_creds_req                      # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.

Insert the credentials into our boto3 session and configure our rasterio environment for data access

Create a boto3 Session object using your temporary credentials. This Session is used to pass credentials and configuration to AWS so we can interact wit S3 objects from applicable buckets.

session = boto3.Session(aws_access_key_id=temp_creds_req['accessKeyId'], 
                        aws_secret_access_key=temp_creds_req['secretAccessKey'],
                        aws_session_token=temp_creds_req['sessionToken'],
                        region_name='us-west-2')

For this exercise, we are going to open up a context manager for the notebook using the rasterio.env module to store the required GDAL and AWS configurations we need to access the data in Earthdata Cloud. While the context manager is open (rio_env.__enter__()) we will be able to run the open or get data commands that would typically be executed within a with statement, thus allowing us to more freely interact with the data. We’ll close the context (rio_env.__exit__()) at the end of the notebook.

GDAL environment variables must be configured to access Earthdata Cloud data assets. Geospatial data access Python packages like rasterio and rioxarray depend on GDAL, leveraging GDAL’s “Virtual File Systems” to read remote files. GDAL has a lot of environment variables that control it’s behavior. Changing these settings can mean the difference being able to access a file or not. They can also have an impact on the performance.

rio_env = rio.Env(AWSSession(session),
                  GDAL_DISABLE_READDIR_ON_OPEN='EMPTY_DIR',
                  GDAL_HTTP_COOKIEFILE=os.path.expanduser('~/cookies.txt'),
                  GDAL_HTTP_COOKIEJAR=os.path.expanduser('~/cookies.txt'))
rio_env.__enter__()

<rasterio.env.Env at 0x7fdb997f6890>

Read in a single HLS file

We’ll access the HLS S3 object using the rioxarray Python package. The package is an extension of xarray and rasterio, allowing users to read in and interact with geospatial data using xarray data structures. We will also be leveraging the tight integration between xarray and dask to lazily read in data via the chunks parameter. This allows us to connect to the HLS S3 object, reading only metadata, an not load the data into memory until we request it via the loads() function.

hls_da = rioxarray.open_rasterio(s3_link, chuncks=True)
hls_da
<xarray.DataArray (band: 1, y: 3660, x: 3660)>
[13395600 values with dtype=int16]
Coordinates:
  * band         (band) int64 1
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red

When GeoTIFFS/Cloud Optimized GeoTIFFS are read in, a band coordinate variable is automatically created (see the print out above). In this exercise we will not use that coordinate variable, so we will remove it using the squeeze() function to avoid confusion.

hls_da = hls_da.squeeze('band', drop=True)
hls_da
<xarray.DataArray (y: 3660, x: 3660)>
[13395600 values with dtype=int16]
Coordinates:
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red

Plot the HLS S3 object

hls_da.hvplot.image(x='x', y='y', cmap='fire', rasterize=True, width=800, height=600, colorbar=True)    # colormaps -> https://holoviews.org/user_guide/Colormaps.html
Unable to display output for mime type(s): 

We can print out the data value as a numpy array by typing .values

hls_da.values

array([[-9999, -9999, -9999, …, 1527, 1440, 1412], [-9999, -9999, -9999, …, 1493, 1476, 1407], [-9999, -9999, -9999, …, 1466, 1438, 1359], …, [-9999, -9999, -9999, …, 1213, 1295, 1159], [-9999, -9999, -9999, …, 1042, 1232, 1185], [-9999, -9999, -9999, …, 954, 1127, 1133]], dtype=int16)

Up to this point, we have not saved anything but metadata into memory. To save or load the data into memory we can call the .load() function.

hls_da_data = hls_da.load()
hls_da_data
<xarray.DataArray (y: 3660, x: 3660)>
array([[-9999, -9999, -9999, ...,  1527,  1440,  1412],
       [-9999, -9999, -9999, ...,  1493,  1476,  1407],
       [-9999, -9999, -9999, ...,  1466,  1438,  1359],
       ...,
       [-9999, -9999, -9999, ...,  1213,  1295,  1159],
       [-9999, -9999, -9999, ...,  1042,  1232,  1185],
       [-9999, -9999, -9999, ...,   954,  1127,  1133]], dtype=int16)
Coordinates:
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red
del(hls_da_data)

Read in and clip a single HLS file

To clip the HLS file, our feature representing our region of interest must be in the same coordinate reference system (CRS) or projection coordinate system as the HLS file. The map projection for our HLS file is Universal Transverse Mercator (UTM) zone 13N. Our feature is mapped to WGS84 geographic coordinate system grid space. We need to transform the geographic coordinate reference system (CRS) of our feature to the UTM projected coordinate system (i.e., UTM Zone 13N)

Read in our geojson file and transform its CRS

field = geopandas.read_file('./data/ne_w_agfields.geojson')

Let’s take a look at the bounding coordinate values.

field_shape = field.geometry[0]
field_shape.bounds

(-101.67271614074707, 41.04754380304359, -101.65344715118408, 41.06213891056728)

Note, the values above are in decimal degrees and represent the longitude and latitude for the lower left corner (-101.67271614074707, 41.04754380304359) and upper right corner (-101.65344715118408, 41.06213891056728) respectively.

Get the projection information from the HLS file

hls_proj = hls_da.rio.crs
hls_proj

CRS.from_epsg(32613)

Transform coordinates from lat lon (units = dd) to UTM (units = m)

geo_CRS = Proj('+proj=longlat +datum=WGS84 +no_defs', preserve_units=True)   # Source coordinate system of the ROI
project = pyproj.Transformer.from_proj(geo_CRS, hls_proj)                    # Set up the transformation
fsUTM = transform(project.transform, field_shape)
fsUTM.bounds

(779588.4994601272, 4549370.366049466, 781270.1479326887, 4551052.979639321)

The coordinates for our feature have now been converted to UTM Zone 13N whether meters is the designated unit. Note the difference in the values between field_shape.bounds (in geographic) and fsUTM.bounds (in UTM projection).

Now we can clip our HLS file to our region of insterest!

Access and clip the HLS file

We can now use our transformed ROI bounding box to clip the HLS S3 object we accessed before. We’ll use the `rio.clip

hls_da_clip = rioxarray.open_rasterio(s3_link, chunks=True).squeeze('band', drop=True).rio.clip([fsUTM])
hls_da_clip
<xarray.DataArray (y: 56, x: 56)>
dask.array<astype, shape=(56, 56), dtype=int16, chunksize=(56, 56), chunktype=numpy.ndarray>
Coordinates:
  * y            (y) float64 4.551e+06 4.551e+06 ... 4.549e+06 4.549e+06
  * x            (x) float64 7.796e+05 7.796e+05 ... 7.812e+05 7.812e+05
    spatial_ref  int64 0
Attributes:
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red
    _FillValue:    -9999
hls_da_clip.hvplot.image(x = 'x', y = 'y', crs = 'EPSG:32613', cmap='fire', rasterize=True, width=800, height=600, colorbar=True)
Unable to display output for mime type(s): 

Read in and clip an HLS time series

Now we’ll read in multiple HLS S3 objects as a time series xarray. Let’s print the links list again to see what we’re working with.

s3_links

[‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021133T172406.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021133T173859.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021140T173021.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021140T172859.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021145T172901.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021156T173029.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021163T173909.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021165T172422.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021165T172901.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021185T172901.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021188T173037.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021190T172859.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021198T173911.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021200T172859.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021203T173909.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021204T173042.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSS30.015/HLS.S30.T13TGF.2021215T172901.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021220T173049.v1.5.B04.tif’, ‘s3://lp-prod-protected/HLSL30.015/HLS.L30.T13TGF.2021229T172441.v1.5.B04.tif’]

Currently, the utilities and packages used in Python to read in GeoTIFF/COG file do not recognize associated dates stored in the internal metadata. To account for the dates for each file we must create a time variable and add it as a dimension in our final time series xarray. We’ll create a function that extracts the date from the file link and create an xarray variable with a time array of datetime objects.

def time_index_from_filenames(file_links):
    '''
    Helper function to create a pandas DatetimeIndex
    '''
    return [datetime.strptime(f.split('.')[-5], '%Y%jT%H%M%S') for f in file_links]
time = xr.Variable('time', time_index_from_filenames(s3_links))

We’ll now specify a chunk size to use that matches the internal tiling of HLS files. This will help improve performance.

chunks=dict(band=1, x=1024, y=1024)

Now, we will create our time series.

hls_ts_da = xr.concat([rioxarray.open_rasterio(f, chunks=chunks).squeeze('band', drop=True) for f in s3_links], dim=time)
hls_ts_da
<xarray.DataArray (time: 19, y: 3660, x: 3660)>
dask.array<concatenate, shape=(19, 3660, 3660), dtype=int16, chunksize=(1, 1024, 1024), chunktype=numpy.ndarray>
Coordinates:
  * x            (x) float64 7e+05 7e+05 7e+05 ... 8.097e+05 8.097e+05 8.097e+05
  * y            (y) float64 4.6e+06 4.6e+06 4.6e+06 ... 4.49e+06 4.49e+06
    spatial_ref  int64 0
  * time         (time) datetime64[ns] 2021-05-13T17:24:06 ... 2021-08-17T17:...
Attributes:
    _FillValue:    -9999.0
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red

Since we used the chunks parameter while reading the data, the hls_ts_da object is read into memory. To do that we’ll use the load() function. But, before that, we’ll clip the hls_ts_da object to our roi using our transformed roi coordinates.

hls_ts_da_clip = hls_ts_da.rio.clip([fsUTM]).load()
hls_ts_da_clip
<xarray.DataArray (time: 19, y: 56, x: 56)>
array([[[-9999, -9999, -9999, ...,   980, -9999, -9999],
        [-9999, -9999, -9999, ...,   287, -9999, -9999],
        [ 1573,  1692,  1708, ...,   410, -9999, -9999],
        ...,
        [-9999, -9999,  1165, ...,  1808,  1869,  1906],
        [-9999, -9999,   989, ..., -9999, -9999, -9999],
        [-9999, -9999,  1085, ..., -9999, -9999, -9999]],

       [[-9999, -9999, -9999, ...,   860, -9999, -9999],
        [-9999, -9999, -9999, ...,   496, -9999, -9999],
        [ 2681,  2773,  2496, ...,   550, -9999, -9999],
        ...,
        [-9999, -9999,  3847, ...,  1997,  1914,  1831],
        [-9999, -9999,  4062, ..., -9999, -9999, -9999],
        [-9999, -9999,  4313, ..., -9999, -9999, -9999]],

       [[-9999, -9999, -9999, ...,   808, -9999, -9999],
        [-9999, -9999, -9999, ...,   230, -9999, -9999],
        [ 1802,  1828,  1863, ...,   306, -9999, -9999],
        ...,
...
        ...,
        [-9999, -9999,  1124, ...,   804,   934,  1008],
        [-9999, -9999,  1003, ..., -9999, -9999, -9999],
        [-9999, -9999,   904, ..., -9999, -9999, -9999]],

       [[-9999, -9999, -9999, ...,  1313, -9999, -9999],
        [-9999, -9999, -9999, ...,  1327, -9999, -9999],
        [ 1091,  1094,  1179, ...,  1223, -9999, -9999],
        ...,
        [-9999, -9999,  1145, ...,  1005,  1097,  1197],
        [-9999, -9999,  1037, ..., -9999, -9999, -9999],
        [-9999, -9999,  1114, ..., -9999, -9999, -9999]],

       [[-9999, -9999, -9999, ...,  1272, -9999, -9999],
        [-9999, -9999, -9999, ...,  1231, -9999, -9999],
        [ 1086,  1105,  1193, ...,  1205, -9999, -9999],
        ...,
        [-9999, -9999,  1045, ...,  1049,  1142,  1219],
        [-9999, -9999,   926, ..., -9999, -9999, -9999],
        [-9999, -9999,  1076, ..., -9999, -9999, -9999]]], dtype=int16)
Coordinates:
  * y            (y) float64 4.551e+06 4.551e+06 ... 4.549e+06 4.549e+06
  * x            (x) float64 7.796e+05 7.796e+05 ... 7.812e+05 7.812e+05
  * time         (time) datetime64[ns] 2021-05-13T17:24:06 ... 2021-08-17T17:...
    spatial_ref  int64 0
Attributes:
    scale_factor:  0.0001
    add_offset:    0.0
    long_name:     Red
    _FillValue:    -9999

Now, we’ll see what we have. Use hvplot to plot the clipped time series

hls_ts_da_clip.hvplot.image(x='x', y='y', width=800, height=600, colorbar=True, cmap='fire').opts(clim=(hls_ts_da_clip.values.min(), hls_ts_da_clip.values.max()))
# Exit our context
rio_env.__exit__()

Resourses

Rough PODAAC ECCO SSH Example

s3_cred_endpoint = {
    'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials',
    'lpdaac':'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'
}
from pystac_client import Client 
podaac_cat = Client.open('https://cmr.earthdata.nasa.gov/stac/POCLOUD/')
search = podaac_cat.search(
    collections=['ECCO_L4_SSH_05DEG_MONTHLY_V4R4'],
    datetime='2015'
)
search.matched()
items = search.get_all_items()
list(items)
ssh_https = items[1].get_assets()['data'].href
ssh_https
ssh_s3 = ssh_https.replace('https://archive.podaac.earthdata.nasa.gov/', 's3://')
ssh_s3
import rasterio as rio
import rioxarray
ssh = rioxarray.open_rasterio(ssh_s3, )
ssh[1].squeeze('band', drop=True)
ssh[0].SSH

Read in geoJSON for subsetting

We will use the input geoJSON file to clip the source data to our desired region of interest.

field = geopandas.read_file('./data/ne_w_agfields.geojson')
fieldShape = field['geometry'][0]  

To clip the source data to our input feature boundary, we need to transform the feature boundary from its original WGS84 coordinate reference system to the projected reference system of the source HLS file (i.e., UTM Zone 13).

foa_url = red_s3_links[0]
with rio.open(foa_url) as src:
    hls_proj = src.crs.to_string()

hls_proj    

Transform geoJSON feature from WGS84 to UTM

geo_CRS = Proj('+proj=longlat +datum=WGS84 +no_defs', preserve_units=True)   # Source coordinate system of the ROI
project = pyproj.Transformer.from_proj(geo_CRS, hls_proj)                    # Set up the transformation
fsUTM = transform(project.transform, fieldShape)

Direct S3 Data Access

Start up a dask client

#from dask.distributed import Client
#client = Client(n_workers=2)
#client

There are multiple way to read COG data in as a time series. The subprocess package is used in this example to run GDAL’s build virtual raster file (gdalbuildvrt) executable outside our python session. First we’ll need to construct a string object with the command and it’s parameter parameters (including our temporary credentials). Then, we run the command using the subprocess.call() function.

Build GDAL VRT Files

Construct the GDAL VRT call
build_red_vrt = f"gdalbuildvrt ./data/red_stack.vrt -separate -input_file_list ./data/S3_T12TGF_RED_VSI_Links.txt --config AWS_ACCESS_KEY_ID {temp_creds_req['accessKeyId']} --config AWS_SECRET_ACCESS_KEY {temp_creds_req['secretAccessKey']} --config AWS_SESSION_TOKEN {temp_creds_req['sessionToken']} --config GDAL_DISABLE_READDIR_ON_OPEN TRUE"
#build_red_vrt    # !!! BEWARE, removing the # on this line will print your temporary S3 credentials.

We now have a fully configured gdalbuildvrt string that we can pass to Python’s subprocess module to run the gdalbuildvrt executable outside our Python environment.

Reading in an HLS time series

We can now read the VRT files into our Python session. A drawback of reading VRTs into Python is that the time coordinate variable needs to be contructed. Below we not only read in the VRT file using rioxarray, but we also repurpose the band variable, which is generated automatically, to hold out time information.

Read the RED VRT in as xarray with Dask backing

%%time

chunks=dict(band=1, x=1024, y=1024)
#chunks=dict(band=1, x=512, y=512)
red = rioxarray.open_rasterio('./data/red_stack.vrt', chunks=chunks)                    # Read in VRT
red = red.rename({'band':'time'})                                                       # Rename the 'band' coordinate variable to 'time' 
red['time'] = [datetime.strptime(x.split('.')[-5], '%Y%jT%H%M%S') for x in links_vsi]   # Extract the time information from the input file names and assign them to the time coordinate variable
red = red.sortby('time')                                                                # Sort by the time coordinate variable
red

Above we use the parameter chunk in the rioxarray.open_rasterio() function to enable the Dask backing. What this allows is lazy reading of the data, which means the data is not actually read in into memory at this point. What we have is an object with some metadata and pointer to the source data. The data will be streamed to us when we call for it, but not stored in memory until with call the Dask compute() or persist() methods.

Clip out the ROI and persist the result in memory

Up until now, we haven’t read any of the HLS data into memory. Now we will use the persist() method to load the data into memory.

red_clip = red.rio.clip([fsUTM]).persist()
red_clip

Above, we persisted the clipped results to memory using the persist() method. This doesn’t necessarily need to be done, but it will substantially improve the performance of the visualization of the time series below.

Plot red_clip with hvplot

red_clip.hvplot.image(x='x', y='y', width=800, height=600, colorbar=True, cmap='Reds').opts(clim=(0.0, red_clip.values.max()))

Read in the NIR and Fmask VRT files

%%time
chunks=dict(band=1, x=1024, y=1024)
nir = rioxarray.open_rasterio('./data/nir_stack.vrt', chunks=chunks)                    # Read in VRT
nir = nir.rename({'band':'time'})                                                       # Rename the 'band' coordinate variable to 'time' 
nir['time'] = [datetime.strptime(x.split('.')[-5], '%Y%jT%H%M%S') for x in links_vsi]   # Extract the time information from the input file names and assign them to the time coordinate variable
nir = nir.sortby('time')                                                                # Sort by the time coordinate variable
nir
%%time
chunks=dict(band=1, x=1024, y=1024)
fmask = rioxarray.open_rasterio('./data/fmask_stack.vrt', chunks=chunks)                    # Read in VRT
fmask = fmask.rename({'band':'time'})                                                       # Rename the 'band' coordinate variable to 'time' 
fmask['time'] = [datetime.strptime(x.split('.')[-5], '%Y%jT%H%M%S') for x in links_vsi]     # Extract the time information from the input file names and assign them to the time coordinate variable
fmask = fmask.sortby('time')                                                                # Sort by the time coordinate variable
fmask

Create an xarray dataset

We will now combine the RED, NIR, and Fmask arrays into a dataset and create/add a new NDVI variable.

hls_ndvi = xr.Dataset({'red': red, 'nir': nir, 'fmask': fmask, 'ndvi': (nir - red) / (nir + red)})
hls_ndvi

Above, we created a new NDVI variable. Now, we will clip and plot our results.

ndvi_clip = hls_ndvi.ndvi.rio.clip([fsUTM]).persist()
ndvi_clip

Plot NDVI

ndvi_clip.hvplot.image(x='x', y='y', groupby='time', width=800, height=600, colorbar=True, cmap='YlGn').opts(clim=(0.0, 1.0))

You may have notices that some images for some of the time step are ‘blurrier’ than other. This is because they are contaminated in some way, be it clouds, cloud shadows, snow, ice.

Apply quality filter

We want to keep NDVI data values where Fmask equals 0 (no clouds, no cloud shadow, no snow/ice, no water.

ndvi_clip_filter = hls_ndvi.ndvi.where(fmask==0, np.nan).rio.clip([fsUTM]).persist()
ndvi_clip_filter.hvplot.image(x='x', y='y', groupby='time', width=800, height=600, colorbar=True, cmap='YlGn').opts(clim=(0.0, 1.0))

Aggregate by month

Finally, we will use xarray’s groupby operation to aggregate by month.

ndvi_clip_filter.groupby('time.month').mean('time').hvplot.image(x = 'x', y = 'y', crs = hls_proj, groupby='month', cmap='YlGn', width=800, height=600, colorbar=True).opts(clim=(0.0, 1.0))
rio_env.__exit__()

References

  • https://rasterio.readthedocs.io/en/latest/
  • https://corteva.github.io/rioxarray/stable/index.html
  • https://tutorial.dask.org/index.html
  • https://examples.dask.org/applications/satellite-imagery-geotiff.html